title: Potential of the aquatic fern Azolla filiculoides in biodegradation of an azo dye: modeling of experimental results by artificial neural networks.
abstract: The potential of an aquatic fern, Azolla filiculoides, in phytoremediation of a mono azo dye solution, C.I. Acid Blue 92 (AB92), was studied. The effects of operational parameters such as reaction time, initial dye concentration, fern fresh weight, pH, temperature and reusability of the fern on biodegradation efficiency were investigated. The intermediate compounds produced by biodegradation process were analyzed using GC-MS analysis. An artificial neural network (ANN) model was developed to predict the biodegradation efficiency. The findings indicated that ANN provides reasonable predictive performance (R2 = 0.961). The effects of AB92 solutions (10 and 20 mg L(-1)) on growth, chlorophylls and carotenoids content, activity of antioxidant enzymes such as superoxide dismutase, peroxidase and catalase and formation of malondialdehyde were analyzed. AB92 generally showed inhibitory effects on the growth. Moreover, photosynthetic pigments in the fronds significantly decreased in the treatments. An increase was detected for lipid peroxidation and antioxidant enzymes activity, suggesting that AB92 caused reactive oxygen species production in Azolla fronds, which were scavenged by induced activities of antioxidant enzymes.
authors.affiliation: Research Laboratory of Advanced Water and Wastewater Treatment Processes, Department of Applied Chemistry, Faculty of Chemistry, University of Tabriz, Tabriz, Iran. a_khataee@tabrizu.ac.ir
authors.forename: A R
authors.initials: AR
authors.lastname: Khataee
authors.forename: A
authors.initials: A
authors.lastname: Movafeghi
authors.forename: F
authors.initials: F
authors.lastname: Vafaei
authors.forename: S Y Salehi
authors.initials: SY
authors.lastname: Lisar
authors.forename: M
authors.initials: M
authors.lastname: Zarei
mesh_terms: D000975:Antioxidants / Q000378:metabolism; D001673:Biodegradation, Environmental; D018533:Biomass; D002338:Carotenoids / Q000032:analysis / Q000378:metabolism; D002374:Catalase / Q000187:drug effects / Q000378:metabolism; D002734:Chlorophyll / Q000032:analysis / Q000378:metabolism; D004396:Coloring Agents / Q000378:metabolism*; D029624:Ferns / Q000187:drug effects / Q000254:growth & development / Q000378:metabolism*; D008401:Gas Chromatography-Mass Spectrometry; D006863:Hydrogen-Ion Concentration; D015227:Lipid Peroxidation / Q000187:drug effects; D009281:Naphthalenes / Q000378:metabolism* / Q000494:pharmacology; D016571:Neural Networks, Computer; D010544:Peroxidases / Q000187:drug effects / Q000378:metabolism; D010940:Plant Proteins / Q000187:drug effects / Q000378:metabolism; D017382:Reactive Oxygen Species / Q000378:metabolism; D013482:Superoxide Dismutase / Q000378:metabolism; D013696:Temperature
chemical_list: C555957:Acid Blue 92; D000975:Antioxidants; D004396:Coloring Agents; D009281:Naphthalenes; D010940:Plant Proteins; D017382:Reactive Oxygen Species; D002734:Chlorophyll; D002338:Carotenoids; D010544:Peroxidases; D002374:Catalase; D013482:Superoxide Dismutase